Data Formats For Inference On The Edge


AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

AI Races To The Edge


AI is becoming increasingly sophisticated and pervasive at the edge, pushing into new application areas and even taking on some of the algorithm training that has been done almost exclusively in large data centers using massive sets of data. There are several key changes behind this shift. The first involves new chip architectures that are focused on processing, moving, and storing data more... » read more

AI Accelerator Architectures Poised For Big Changes


AI is driving a frenzy of activity in the chip world as companies across the semiconductor ecosystem race to include AI in their product lineup. The challenge now is how to make AI run faster, use less energy, and to be able to leverage it from the edge to the data center — particularly with the rollout of large language models. On the hardware side, there are two main approaches for accel... » read more

EDA Pushes Deeper Into AI


EDA vendors are ramping up the use of AI/ML in their tools to help chipmakers and systems companies differentiate their products. In some cases, that means using AI to design AI chips, where the number and breadth of features and potential problems is exploding. What remains to be seen is how well these AI-designed chips behave over time, and where exactly AI benefits design teams. And all o... » read more

Considerations For Accelerating On-Device Stable Diffusion Models


One of the more powerful – and visually stunning – advances in generative AI has been the development of Stable Diffusion models. These models are used for image generation, image denoising, inpainting (reconstructing missing regions in an image), outpainting (generating new pixels that seamlessly extend an image's existing bounds), and bit diffusion. Stable Diffusion uses a type of dif... » read more

Flipping Processor Design On Its Head


AI is changing processor design in fundamental ways, combining customized processing elements for specific AI workloads with more traditional processors for other tasks. But the tradeoffs are increasingly confusing, complex, and challenging to manage. For example, workloads can change faster than the time it takes to churn out customized designs. In addition, the AI-specific processes may ex... » read more

Unlocking The Power Of Edge Computing With Large Language Models


In recent years, Large Language Models (LLMs) have revolutionized the field of artificial intelligence, transforming how we interact with devices and the possibilities of what machines can achieve. These models have demonstrated remarkable natural language understanding and generation abilities, making them indispensable for various applications. However, LLMs are incredibly resource-intensi... » read more

Chip Industry Week In Review


By Susan Rambo, Karen Heyman, and Liz Allan The Biden-Harris administration designated 31 Tech Hubs across the U.S. this week, focused on industries including autonomous systems, quantum computing, biotechnology, precision medicine, clean energy advancement, and semiconductor manufacturing. The Department of Commerce (DOC) also launched its second Tech Hubs Notice of Funding Opportunity. ... » read more

Making Connections In 3D Heterogeneous Integration


Activity around 3D heterogeneous integration (3DHI) is heating up, driven by growing support from governments, the need to add more features and compute elements into systems, and a widespread recognition that there are better paths forward than packing everything into a single SoC at the same process node. The leading edge of chip design has changed dramatically over the last few years. Int... » read more

Partitioning Processors For AI Workloads


Partitioning in complex chips is beginning to resemble a high-stakes guessing game, where choices need to extrapolate from what is known today to what is expected by the time a chip finally ships. Partitioning of workloads used to be a straightforward task, although not necessarily a simple one. It depended on how a device was expected to be used, the various compute, storage and data paths ... » read more

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